Loading...
Please wait, while we are loading the content...
Similar Documents
MIC framework: An information-theoretic approach to quantitative association rule mining (2006)
| Content Provider | CiteSeerX |
|---|---|
| Author | Ke, Yiping Cheng, James Ng, Wilfred |
| Description | We propose a framework, called MIC, which adopts an information-theoretic approach to address the problem of quantitative association rule mining. In our MIC frame-work, we first discretize the quantitative attributes. Then, we compute the normalized mutual information between the attributes to construct a graph that indicates the strong informative-relationship between the attributes. We utilize the cliques in the graph to prune the unpromising attribute sets and hence the joined intervals between these attributes. Our experimental results show that the MIC framework sig-nificantly improves the mining speed. Importantly, we are able to obtain most of the high-confidence rules and the missing rules are shown to be less interesting. 1. |
| File Format | |
| Language | English |
| Publisher Date | 2006-01-01 |
| Access Restriction | Open |
| Subject Keyword | Mic Framework Association Rule Mining Normalized Mutual Information Joined Interval Unpromising Attribute Set Mic Frame-work High-confidence Rule Strong Informative-relationship Quantitative Association Rule Mining In ICDE Mining Speed Quantitative Attribute Experimental Result Missing Rule Information-theoretic Approach |
| Content Type | Text |
| Resource Type | Article |
